In this article, we propose a novel approach to achieve spectrum prediction, parameter fitting, inverse design and performance optimization for the plasmonic waveguide coupled with cavities structure (PWCCS) based on artificial neural networks (ANNs). The Fano resonance and plasmon induced transparency effect originated from the PWCCS have been selected as illustrations to verify the effectiveness of ANNs. We use the genetic algorithm to design the network architecture and select the hyper-parameters for ANNs. Once ANNs are trained by using a small sampling of the data generated by Monte Carlo method, the transmission spectrums predicted by the ANNs are quite approximate to the simulated results. The physical mechanisms behind the phenomena are discussed theoretically, and the uncertain parameters in the theoretical models are fitted by utilizing the trained ANNs. More importantly, our results demonstrate that this model-driven method not only realizes the inverse design of the PWCCS with high precision but also optimizes some critical performance metrics for transmission spectrum. Compared with previous works, we construct a novel model-driven analysis method for the PWCCS which are expected to have significant applications in the device design, performance optimization, variability analysis, defect detection, theoretical modeling, optical interconnects and so on.

Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks

High sensitivity label-free refractometer based biosensor applicable to glycated hemoglobin detection in human blood using all-circular photonic crystal ring resonators

In this paper, we propose a novel approach to achieve spectrum prediction, parameter fitting, inverse design, and performance optimization for the plasmonic waveguide-coupled with cavities structure (PWCCS) based on artificial neural networks (ANNs). The Fano resonance and plasmon-induced transparency effect originated from the PWCCS have been selected as illustrations to verify the effectiveness of ANNs. We use the genetic algorithm to design the network architecture and select the hyperparameters for ANNs. Once ANNs are trained by using a small sampling of the data generated by the Monte Carlo method, the transmission spectra predicted by the ANNs are quite approximate to the simulated results. The physical mechanisms behind the phenomena are discussed theoretically, and the uncertain parameters in the theoretical models are fitted by utilizing the trained ANNs. More importantly, our results demonstrate that this model-driven method not only realizes the inverse design of the PWCCS with high precision but also optimizes some critical performance metrics for the transmission spectrum. Compared with previous works, we construct a novel model-driven analysis method for the PWCCS that is expected to have significant applications in the device design, performance optimization, variability analysis, defect detection, theoretical modeling, optical interconnects, and so on.

/pdf/efficient-spectrum-prediction-and-inverse-design-for-4tfplzxs2h.pdf

A nanoscale refractive index sensor comprising of input–output graphene parallel plane waveguides coupled through a resonator is proposed. Operating as a wavelength selective structure in mid-infrared region, the sensor performance is based on the variation of the resonance wavelength of the structure. The simulation results obtained by numerical method of the finite difference time domain reveal linear dependency between the resonance wavelengths and the refractive indices of the material injected into the coupling resonator of the sensor, predictable by the resonance criterion of the resonator. The wavelength resolution of the detection system determines the overall sensor resolution. To utilize voltage dependency of graphene chemical potential, the wavelength range of the performance of the sensor can be tuned appropriately. The proposed sensor can be used as a platform for design of the sensors utilizing in various chemical and biomedical systems.

Tunable Mid-Infrared Nanoscale Graphene-Based Refractive Index Sensor

Photonic crystals are nanoscale structures that affect the motion of photons. The strong light limitation in photonic crystals and the adjustment of its structural parameters have led to the emergence of photonic crystal biosensors. Moreover, the use of holes as a feature of photonic crystals has resulted in sensors that are very sensitive to low refractive index changes with a small sensing area, which offers flexibility and integration on single-chip systems. Using emerging optofluidic technology, label-free biosensors are on the rise. In this review, we examine various types of photonic crystal sensors, such as waveguides, nanoresonators, LX resonators, holes, multi-channel resonators, nano RINGS resonators, and fibers. These sensors are based on the measurement of biomolecules and the refractive index properties that have been identified. Finally, a variety of challenges and guidelines for the construction of label-free diagnostic biosensors are examined.

Two-Dimensional photonic crystal Biosensors: A review

Periodically nanostructured optical coatings for the manipulation of laser radiation

In this manuscript, an evolutionary algorithm is applied to design a mode order converter photonic crystal structure for achieving transformation of the propagating fundamental mode to the higher order mode. In order to transform incident light mode from an even mode to an odd mode, predefined mask object functions are employed. The cross-sectional field profiles at the output face of the optimized structure are utilized to evaluate the optimization cost function to achieve mode order transformation task. In the study, the optimized structure composed of dielectric alumina (Al 2 O 3 ) rods and time domain analyses of the optimization process are done by using finite-difference time domain method. Furthermore, numerical results are confirmed by experiments for designed structure at the microwave frequencies. It is shown that the numerical calculations and experimental measurements agree well with each other.

All Dielectric Mode Order Transformation Photonic Structure Design by Evolutionary Optimization Approach

Increasing light absorption in ultrathin-film silicon solar cells is important to improve efficiency and reduce costs. In this study, multi-objective particle swarm optimization (MOPSO) is employed to find proper solar cell parameters for minimum reflection and maximum light trapping which give rise to enhanced absorption. Numerical investigations for two different surface patterns (rectangular and pyramid) are conducted. The results show that, for the ultrathin-film silicon solar cell having an equivalent thickness of 2 μm, random surfaced pyramid structure provides an ideal photocurrent of 36.96 mA/cm2 and random surfaced rectangular structure provides an ideal photocurrent of 34.67 mA/cm2. From this point of view, proposed approach can be applied to various semiconductor film thicknesses by providing robustness against metallic loss at the back plate of the solar cells.

Surface Texturing with Multi-objective Particle Swarm Optimization for Absorption Enhancement in Silicon Photovoltaics

This study presents active beam steering and afocal zooming of light by incorporating liquid crystals (LCs) with graded index photonic crystal (GRIN PC). The GRIN PC structures are composed of low refractive index polymer annular rods having gradually varied radii of holes. To actively manipulate incident light, the annular rods are infiltrated with nematic LCs. By applying an external voltage to the infiltrated LCs, the effective index profile of the low-index GRIN PC structure is modulated without introducing any mechanical movement. The incident beam deflection and corresponding focal distance modulation are tuned only by controlling the applied bias voltage. In the present work, hyperbolic secant refractive index profile is chosen to design GRIN PC structures. To design GRIN PC structure with annular PCs, Maxwell-Garnett effective medium approximation is employed. Moreover, we analytically express the relation between infiltrated LCs and gradient parameter to show the physical background of the tuning ability of the proposed devices. Also, beam steering and afocal zooming devices are analytically investigated via geometrical optics and numerically realized with the help of the finite-difference time-domain method. A beam deflection with an angle change of {\Delta}{\theta}out= 44° and a light magnification with maximum x2.15 are obtained. LCs are inexpensive materials and work under low voltage/power condition. This feature can be used for designing an electro-optic GRIN PC device that can be useful in various optical applications.

Ceren Babayigit

Papers

Periodically nanostructured optical coatings for the manipulation of laser radiation

All Dielectric Mode Order Transformation Photonic Structure Design by Evolutionary Optimization Approach

Surface Texturing with Multi-objective Particle Swarm Optimization for Absorption Enhancement in Silicon Photovoltaics

Active beam steering and afocal zooming by nematic liquid crystal infiltrated graded index photonic structures

Dielectric metasurfaces for spatial filtering and polarization control